Provision for Loan Loss

The FDIC reported in the first quarter of 2011 that provision for loan losses fell less than half compared to the previous year. It reports that provisions for loan losses fell to $20.7 billion in the first quarter from $51.6 billion a year earlier. This marks the sixth quarter in a row that loss provisions have had a year-over-year decline. It is the smallest quarterly loss provision for the industry since third quarter 2007. The largest reductions in provisions occurred at credit card lenders that made sizable additions to their loan-loss reserves a year ago, but almost half of all institutions (48.9 percent) reported lower provisions. Less than a third (32.6 percent) increased their provisions from year-earlier levels.

Provisions are considered as an earning management tool by banks and regulators alike. Provision for losses is created by a charge to earnings and parameters are used to create them depending on the credit portfolio. Post the 80’s after the loans crisis banks increased their provisions for losses also known as allowances. However in the 90’s regulators criticized banks for over budgeting for provisions and using them to smooth income to reduce variances in income. Cut to the 2008 financial crisis banks are being blamed for not provisioning considering their large exposures to poor quality debt. Clearly this is a fine balance difficult to perfect and one that must be monitored and managed from time to time. Provisioning cannot be fitted neatly into a mathematical calculation; it needs to be based on judgment.

Banks need to take into account credit quality, current trends rather than projections and macro factors like industry conditions, global risk factors and market conditions. Credit segmentation and therefore requisite levels for each can then be arrived at.

Provisioning at a general level is made at portfolio level. It is the difference between loss charges priced into the product and portfolio expected loss.

In the case of large transactions some more factors are added for provisions. In transactions of low volume where market prices can be observed and a drop in price when transaction volumes increase liquidity reserves are made. In the case of non- standard derivatives, individual provisions that cover model risk are also made. It is important that the reserves be monitored and adjusted where the case may be as expected losses vary due to changes to exposure and default probabilities.

A credit officer would have a list of transactions whose provisions he would monitor often. He will observe if adjustments are required in individual reserves based on ratings. He will check if realistic assumptions have been made for the correlation for joint obligor defaults. He will also check if individual credit protection offsets the amount of risk that it is supposed to cover. Market price information like CDS spreads is also used to compute provision for losses.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $29 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.